Graph Neural Networks with Multi-features for Predicting Cocrystals using APIs and Coformers Interactions.

Mswahili, Medard Edmund; Jo, Kyuri; Lee, SeungDong; Jeong, Young-Seob

Mswahili, Medard Edmund; Jo, Kyuri; Lee, SeungDong; Jeong, Young-Seob.

Afiliación

Mswahili ME; Department of Computer Engineering, Chungbuk National University, Cheongju, 28644, South Korea.
Jo K; Department of Computer Engineering, Chungbuk National University, Cheongju, 28644, South Korea.
Lee S; Department of Computer Engineering, Chungbuk National University, Cheongju, 28644, South Korea.
Jeong YS; Department of Computer Engineering, Chungbuk National University, Cheongju, 28644, South Korea.

Curr Med Chem ; 31(36): 5953-5968, 2024.

Article en En | MEDLINE | ID: mdl-38847382

ABSTRACT

ABSTRACT

INTRODUCTION:

Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals when reacting with coformers, as a potential and attractive route for drug substance development.

METHODS:

However, screening and selecting suitable and appropriate coformers that may potentially react with APIs to successfully form cocrystals is a time-consuming, inefficient, economically expensive, and labour-intensive task. In this study, we implemented GNNs to predict the formation of cocrystals using our introduced API-coformers relational graph data. We further compared our work with previous studies that implemented descriptor-based models (e.g., random forest, support vector machine, extreme gradient boosting, and artificial neural networks).

RESULTS:

All built graph-based models show compelling performance accuracies (i.e., 91.36, 94.60 and 95. 95% for GCN, GraphSAGE, and RGCN respectively). RGCN demonstrated effectiveness and prevailed among the built graph-based models due to its capability to capture intricate and learn nuanced relationships between entities such as non-ionic and non-covalent interactions or link information between APIs and coformers which are crucial for accurate predictions and representations.

CONCLUSION:

These capabilities allows the model to adeptly learn the topological structure inherent in the graph data.

Asunto(s)

Cristalización; Redes Neurales de la Computación; Preparaciones Farmacéuticas/química

Palabras clave

Graph neural network; active pharmaceutical ingredients (APIs); cocrystals formation; coformers.; deep learning; drug discovery and development; machine learning; molecular descriptors

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Cristalización Idioma: En Revista: Curr Med Chem Asunto de la revista: QUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Emiratos Árabes Unidos

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